GlioblastomaARTPLAN-GLIONCT06657027
Artificial Intelligence-Guided Radiotherapy Planning for Glioblastoma
An observational study of Glioblastoma, run by Hospital del Rio Hortega.
Trial summary
The ARTPLAN-GLIO study aims to evaluate the feasibility and effectiveness of integrating artificial intelligence in personalized radiotherapy planning for glioblastomas. On the basis of previous work by our group, where a predictive model was developed from radiological characteristics extracted from MR images, this… Read more about this study
- Who is it for?
- Ages 15 years and older
- Conditions the study is for
- Glioblastoma
- How many people
- 40 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2025to 30 Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 15 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population consists of adult patients with newly diagnosed IDH wild-type glioblastoma, grade 4, as classified by the World Health Organization (WHO) 2021 Central Nervous System Tumor guidelines. Eligible participants must have undergone maximum safe tumor resection and be scheduled to receive radiotherapy.
Who may be able to join
- Patients with a recent diagnosis of IDH wild-type glioblastoma, grade 4 according to the Central Nervous System Tumors classification of the World Health Organization of 2021.
- Ability to undergo MRI studies.
- Performance status with Karnofsky Performance Status (KPS) ≥ 60.
- Life expectancy ≥ 12 weeks.
- Laboratory results within the following ranges, obtained in the 14 days prior to enrollment:
- Leucocitos ≥ 3,000/µL.
Show all 15Show fewer
- Absolute neutrophils ≥ 1,500/µL.
- Plaquetas ≥ 75,000/µL.
- Hemoglobin ≥ 9.0 g/dL (transfusion is allowed to reach the minimum level).
- Glutamic-oxaloacetic transaminase (SGOT) ≤ 2 times the upper limit of normal.
- Bilirubin ≤ 2 times the upper limit of normal.
- Creatinina ≤ 1.5 mg/dL.
- Women of childbearing age must present a negative pregnancy test ≤ 14 days prior to enrollment.
- Ability to understand and sign the informed consent.
- Willingness to refrain from other cytotoxic or noncytotoxic therapies against the tumor during the protocol.
Who cannot join
- Presence of pacemakers, neurostimulators, cochlear implants, metal in ocular structures, or work history that compromise safety in MRI.
- Significant medical illnesses that may compromise tolerance to treatment, at the discretion of the investigator.
- History of invasive cancer in the last 3 years, with few exceptions.
- Active infections or serious intercurrent illnesses.
- Previous treatments with cytotoxic, noncytotoxic, experimental agents, or cranial radiation therapy.
- Maximum radiation target volume (GTV3) greater than 65 cc.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
Santiago Cepeda Principal Investigator, MD., PhD contact
About this study
Official title: Evaluation of the Efficacy and Safety of Personalized Radiotherapy Guided by Predictive Models of Tumor Infiltration, Combining Artificial Intelligence and Multiparametric MRI in Glioblastomas
The ARTPLAN-GLIO study aims to evaluate the feasibility and effectiveness of integrating artificial intelligence in personalized radiotherapy planning for glioblastomas. On the basis of previous work by our group, where a predictive model was developed from radiological characteristics extracted from MR images, this project will evaluate the use of tumor infiltration probability maps in radiotherapy planning.
Currently, radiotherapy treatment uses margins defined by population studies, without considering the individual characteristics of the patients. Although 80% of recurrences occur in peritumoral areas close to the surgical margins, treatment volumes are not customized owing to the lack of techniques that distinguish between edema and infiltrated tumor tissue.
Read more
Our recurrence probability maps address this limitation and could improve radiation planning. In this study, the volumes and doses of radiotherapy were adjusted according to the predictions of the model, with a focus on high-risk areas to optimize local control and reduce toxicity in healthy tissues.
Survival results will be compared between patients treated with personalized AI-guided radiotherapy and a historical cohort with standard treatment. In addition, the safety of the approach will be evaluated by adverse event analysis. Finally, an accessible online platform with the potential to transform glioblastoma treatment and improve patient survival will be developed to implement this predictive model.
- Conditions
- Glioblastoma
- Keywords
- radiotherapy, oncology, artificial intelligence, glioblastoma, personalized radiotherapy, precision medicine
- Medical subject headings
- Glioblastoma, Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| AI-Guided Radiotherapy Cohort | This cohort includes patients with newly diagnosed IDH wild-type glioblastoma, grade 4, according to the 2021 WHO classification of Central Nervous System Tumors. Patients in this group will undergo personalized radiotherapy guided by artificial intelligence (AI) and multiparametric MRI, using predictive models to adjust treatment volumes and doses according to areas of tumor infiltration. The AI model, developed from radiomic characteristics of postoperative MRI, predicts tumor recurrence and infiltration, enabling targeted dose escalation to high-risk areas while minimizing radiation exposure to healthy tissues. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Feasibility of AI-Guided Radiotherapy for Glioblastoma
The primary outcome of the study is to assess the feasibility of integrating an AI-based predictive model into radiotherapy planning for patients with glioblastoma. The model uses radiomic features derived from multiparametric MRI to generate tumor infiltration probability maps, which guide the personalized adjustment of treatment volumes and doses. Feasibility will be determined by evaluating the successful integration of the AI model into clinical practice, the precision of the model in identifying areas of tumor infiltration, and the ability to implement personalized treatment plans in a routine clinical setting.
Measured: 12 months after the start of radiotherapy for the last enrolled patient.
Secondary outcomes (3)
Progression-Free Survival (PFS) at 1 Year
This outcome evaluates whether patients treated with personalized AI-guided radiotherapy experience improved progression-free survival (PFS) at one year compared to a historical control group treated with standard radiotherapy. PFS is defined as the time from the start of radiotherapy to either the first documented disease progression or death from any cause. The AI-guided approach uses tumor infiltration probability maps to target high-risk areas, aiming to delay or prevent local recurrence.
Measured: 12 months after the start of radiotherapy for each patient.
Overall Survival (OS)
This outcome measures the overall survival (OS) of patients treated with AI-guided personalized radiotherapy for glioblastoma. OS is defined as the time from the start of radiotherapy to death from any cause. The study aims to assess whether personalized radiotherapy, guided by AI-driven tumor infiltration probability maps, improves survival outcomes compared to standard radiotherapy. This will be evaluated by comparing OS in the AI-guided group with a historical control group treated with standard radiotherapy protocols.
Measured: 24 months after the start of radiotherapy for each patient.
Quality of Life
This outcome evaluates the differences in quality of life (QoL) between patients treated with AI-guided radiotherapy based on multiparametric MRI and those treated with standard radiotherapy (historical controls). Quality of life will be evaluated using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30).
Measured: 12 months after the start of radiotherapy for each patient.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Oct 2024
- Study start1 Jan 2025 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion30 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for GlioblastomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hospital del Rio Hortega
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Santiago Cepeda, Hospital del Rio Hortega
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06657027
- Sponsor's protocol ID
- PI-24-563-H
- Acronym
- ARTPLAN-GLIO
- Status last verified
- Oct 2024
- First submitted
- 15 Oct 2024
- Last updated
- 24 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Cepeda S, Luppino LT, Perez-Nunez A, Solheim O, Garcia-Garcia S, Velasco-Casares M, Karlberg A, Eikenes L, Sarabia R, Arrese I, Zamora T, Gonzalez P, Jimenez-Roldan L, Kuttner S. Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers (Basel). 2023 Mar 22;15(6):1894. doi: 10.3390/cancers15061894.
PubMed 36980783
Study at a glance
From the registry record, last updated by the sponsor on 24 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of the Efficacy and Safety of Personalized Radiotherapy Guided by Predictive Models of Tumor Infiltration, Combining Artificial Intelligence and Multiparametric MRI in Glioblastomas
- ClinicalTrials.gov ID
- NCT06657027
- Acronym / sponsor ID
- ARTPLAN-GLIO / PI-24-563-H
- Conditions
- Glioblastoma
- Keywords
- radiotherapy, oncology, artificial intelligence, glioblastoma, personalized radiotherapy, precision medicine
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- Hospital del Rio HortegaUniversity, hospital or other
- Study start
- 1 Jan 2025 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 30 Jun 2027 (estimated)
- First posted
- 24 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The ARTPLAN-GLIO study aims to evaluate the feasibility and effectiveness of integrating artificial intelligence in personalized radiotherapy planning for glioblastomas. On the basis of previous work by our group, where a predictive model was developed from radiological characteristics extracted from MR images, this project will evaluate the use of tumor infiltration probability maps in radiotherapy planning.
Currently, radiotherapy treatment uses margins defined by population studies, without considering the individual characteristics of the patients. Although 80% of recurrences occur in peritumoral areas close to the surgical margins, treatment volumes are not customized owing to the lack of techniques that distinguish between edema and infiltrated tumor tissue.
Our recurrence probability maps address this limitation and could improve radiation planning. In this study, the volumes and doses of radiotherapy were adjusted according to the predictions of the model, with a focus on high-risk areas to optimize local control and reduce toxicity in healthy tissues.
Survival results will be compared between patients treated with personalized AI-guided radiotherapy and a historical cohort with standard treatment. In addition, the safety of the approach will be evaluated by adverse event analysis. Finally, an accessible online platform with the potential to transform glioblastoma treatment and improve patient survival will be developed to implement this predictive model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-Guided Radiotherapy Cohort | – | –This cohort includes patients with newly diagnosed IDH wild-type glioblastoma, grade 4, according to the 2021 WHO classification of Central Nervous System Tumors. Patients in this group will undergo personalized radiotherapy guided by artificial intelligence (AI) and multiparametric MRI, using predictive models to adjust treatment volumes and doses according to areas of tumor infiltration. The AI model, developed from radiomic characteristics of postoperative MRI, predicts tumor recurrence and infiltration, enabling targeted dose escalation to high-risk areas while minimizing radiation exposure to healthy tissues. |
Who may qualify
- Ages
- 15 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population consists of adult patients with newly diagnosed IDH wild-type glioblastoma, grade 4, as classified by the World Health Organization (WHO) 2021 Central Nervous System Tumor guidelines. Eligible participants must have undergone maximum safe tumor resection and be scheduled to receive radiotherapy.
Key inclusion criteria (15 listed)
- Patients with a recent diagnosis of IDH wild-type glioblastoma, grade 4 according to the Central Nervous System Tumors classification of the World Health Organization of 2021.
- Ability to undergo MRI studies.
- Performance status with Karnofsky Performance Status (KPS) ≥ 60.
- Life expectancy ≥ 12 weeks.
- Laboratory results within the following ranges, obtained in the 14 days prior to enrollment:
Key exclusion criteria (6 listed)
- Presence of pacemakers, neurostimulators, cochlear implants, metal in ocular structures, or work history that compromise safety in MRI.
- Significant medical illnesses that may compromise tolerance to treatment, at the discretion of the investigator.
- History of invasive cancer in the last 3 years, with few exceptions.
- Active infections or serious intercurrent illnesses.
- Previous treatments with cytotoxic, noncytotoxic, experimental agents, or cranial radiation therapy.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Feasibility of AI-Guided Radiotherapy for Glioblastoma
Time frame: 12 months after the start of radiotherapy for the last enrolled patient.
The primary outcome of the study is to assess the feasibility of integrating an AI-based predictive model into radiotherapy planning for patients with glioblastoma. The model uses radiomic features derived from multiparametric MRI to generate tumor infiltration probability maps, which guide the personalized adjustment of treatment volumes and doses. Feasibility will be determined by evaluating the successful integration of the AI model into clinical practice, the precision of the model in identifying areas of tumor infiltration, and the ability to implement personalized treatment plans in a routine clinical setting.
3 secondary outcomes
Progression-Free Survival (PFS) at 1 Year
Time frame: 12 months after the start of radiotherapy for each patient.
Overall Survival (OS)
Time frame: 24 months after the start of radiotherapy for each patient.
Quality of Life
Time frame: 12 months after the start of radiotherapy for each patient.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Oct 2024.
- Background: PubMed 36980783Cepeda S, Luppino LT, Perez-Nunez A, Solheim O, Garcia-Garcia S, Velasco-Casares M, Karlberg A, Eikenes L, Sarabia R, Arrese I, Zamora T, Gonzalez P, Jimenez-Roldan L, Kuttner S. Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers (Basel). 2023 Mar 22;15(6):1894. doi: 10.3390/cancers15061894.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Santiago Cepeda Principal Investigator, MD., PhD contact
Questions about this trial
What is this clinical trial studying?
An observational study of Glioblastoma, run by Hospital del Rio Hortega.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 15 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 24 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Santiago Cepeda Principal Investigator, MD., PhD (+34983420400 ext. 85954, scepedac@saludcastillayleon.es), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Feasibility of AI-Guided Radiotherapy for Glioblastoma (time frame: 12 months after the start of radiotherapy for the last enrolled patient.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06657027, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Oct 2024.